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Ultrasound (US) has been introduced to computer-assisted orthopedic surgery for bone registration owing to its advantages of nonionizing radiation, low cost, and noninvasiveness. However, the registration accuracy is limited by US image distortion caused by variations in the acoustic properties of soft tissues. This paper proposes a soft-tissue sound-speed-aware registration method to overcome the above challenge. First, the feature enhancement strategy of multi-channel overlay is proposed for U 2 -net to improve bone segmentation performance. Secondly, the sound speed of soft tissue is estimated by simulating the bone surface distance map for the update of US-derived points. Finally, an iterative registration strategy is adopted to optimize the registration result. A phantom experiment was conducted using different registration methods for the femur and tibia/fibula. The fiducial registration error (femur, 0.98 ± 0.08 mm (mean ± SD); tibia/fibula, 1.29 ± 0.19 mm) and the target registration error (less than 2.11 mm) showed the high accuracy of the proposed method. The experimental results suggest that the proposed method can be integrated into navigation systems that provide surgeons with accurate 3D navigation information., (© 2024. International Federation for Medical and Biological Engineering.)

Background: The regions of the liver with cholestasis caused by biliary tumors or thrombosis can be distinctly identified using indocyanine green (ICG) fluorescence imaging. 1 The authors' team reported the application of bile-duct obstructed area imaging (BOAI) to assist open hepatectomy for intrahepatic cholangiocarcinoma (ICC) combined with intrahepatic bile duct obstruction previously. 2 This video is the first report of real-time BOAI-guided three-dimensional (3D) laparoscopic hepatectomy using a 3D-4K fluorescence imaging system., Methods: A 65-year-old man was admitted to the authors' institution with clonorchiasis. Preoperative computed tomography (CT) and magnetic resonance cholangiopancreatography (MRCP) showed an obstruction and diffuse dilation of the right hepatic duct. A 15-min retention of ICG (ICG R15) was performed 5 days before the operation, with a 3.3% result. Preoperative planning involved performing laparoscopic right hemi-hepatectomy with regional lymph node dissection assisted by visualization technology. 3 During the procedure, significant fluorescence accumulation in the right liver was shown by fluorescence imaging. With the guidance of real-time BOAI (Fig. 1), the regions of biliary obstruction were precisely resected, and the middle hepatic vein (MHV) was passively and adequately exposed on the cutting plane. Fig. 1 Administration steps for real-time bile duct-obstructed area imaging. A ICG is injected intravenously 3-5 days before operation at a dose of 0.5 mg/kg. B ICG is accumulated in the whole liver within a few minutes after injection. C ICG is selectively absorbed by the liver and excreted into the intestines, whereby it is retained in areas of biliary obstruction RESULTS: The histopathologic diagnosis indicated high-grade intraepithelial neoplasia of the right bile duct tumor without lymph node metastases and clonorchiasis. The duration of the operation was 300 min, with an intraoperative blood loss of 50 ml. No postoperative complications occurred, and the patient was discharged after 7 days., Conclusion: Laparoscopic right hemi-hepatectomy for the bile-duct obstructed area with the guidance of real-time BOAI is feasible and effective., (© 2024. Society of Surgical Oncology.)

Purpose: Collimator-detector response function (CDRF) of a SPECT scanner refers to the image generated from a point source of activity. This research aims to characterize the CDRF of a breast-dedicated SPECT imager equipped with a lofthole collimator using GATE Monte Carlo simulation., Materials and Methods: To do so, a cylindrical multi-lofthole collimation system with lofthole apertures dedicated to breast imaging was modeled using the GATE Monte Carlo simulator. The dependency of the CDRF on the source-to-collimator distance of a single-lofthole as well as 8-lofthole collimations was assessed and then compared. In addition, the 3D-sensitivity map of the 8-lofthole collimation was derived. Finally, fair comparisons were conducted between the response of the 8-lofthole collimator and that of an 8-pinhole and also existing analytical derivations. In all cases, a data acquisition period of 5.0 min with an in-air 99m Tc point source was considered., Results: For the single-lofthole collimator, 4.5 times increasing the magnification factor leads to a 16- and twofold improvement in the sensitivity and spatial resolution, respectively. In the single-lofthole collimator, the resolution and sensitivity are degraded as the source-to-aperture distance increases. For the cylindrical 8-lofthole collimator, the findings confirm that CDRF strongly depends on source-to-aperture distance and angle of photon incidence. For a 30 mm in-plane offset point, a 25% increase in sensitivity is observed compared to that of the center of the FOV. Increasing the angle from 0 ∘ to 34 ∘ results in a 50% reduction in sensitivity. Furthermore, the findings illustrate that spatial resolution follows a quadratic function as 10 - 3 d 2 + 2 × 10 - 4 d + R 0 where d is an offset along the x-, y-, and z-axis, and R 0 is the spatial resolution at the center of the FOV., Conclusion: In conclusion, both spatial resolution and sensitivity of the lofthole collimation are considerably angle- and offset-dependent within the FOV of single- and multi-lofthole collimated SPECT imagers., (© 2024. The Author(s) under exclusive licence to Japan Radiological Society.)

Purpose: The aim of this study was to compare the accuracy of colonoscopy (CS) and CT colonography (CTC) in the measurement of colorectal polyps using pathological size as a reference., Materials and Methods: The analysis included 61 colorectal polyps in 28 patients who underwent preoperative CTC at our institution. All polyps were endoscopically resected. Polyp sizes were measured by CS and CTC. Endoscopic polyp size was extracted from endoscopy records written by one of two endoscopists (A with 11 and B with 6 years of endoscopic experience, respectively), who estimated the size visually/categorically without any measuring devices. After matching the location, the polyp size was measured on CTC using manual three-dimensional (3D) measurement on a workstation. The sizes of resected polyps were also measured after pathological inspection. Differences of the polyp size between CTC and histology, and between CS and histology were compared using paired t tests. Differences in measurement between the two endoscopists were also analyzed., Results: The mean diameters of polyps measured using CS, CTC, and pathology were 10.5 mm, 9.2 mm, and 8.4 mm, respectively. There was a significant correlation between CS and pathology, as well as between CTC and pathology (both P < 0.0001). The correlation coefficient for CS (r = 0.86) was lower than that for CTC (r = 0.96). The correlations between CS and pathology for endoscopists A and B were 0.90 and 0.89, respectively., Conclusion: Measurements of polyp size using CTC were closer to the pathological measurements compared to those by CS, which exhibited greater variability. This suggests that CTC may be more suitable for polyp size measurements in the clinical setting if patients undergo CTC concurrently with colonoscopy., (© 2024. The Author(s).)

Purpose: This research aimed to develop an innovative method for designing and fabricating nasal prostheses that reduces anaplastologist expertise dependency while maintaining quality and appearance, allowing patients to regain their normal facial appearance., Methods: The method involved statistical shape modeling using a morphable face model and 3D data acquired through optical scanning or CT. An automated design process generated patient-specific fits and appearances using regular prosthesis materials and 3D printing of molds. Manual input was required for specific case-related details., Results: The developed method met all predefined requirements, replacing analog impression-making and offering compatibility with various data acquisition methods. Prostheses created through this method exhibited equivalent aesthetics to conventionally fabricated ones while reducing the skill dependency typically associated with prosthetic design and fabrication., Conclusions: This method provides a promising approach for both temporary and definitive nasal prostheses, with the potential for remote prosthesis fabrication in areas lacking anaplastology care. While new skills are required for data acquisition and algorithm control, these technologies are increasingly accessible. Further clinical studies will help validate its effectiveness, and ongoing technological advancements may lead to even more advanced and skill-independent prosthesis fabrication methods in the future., (© 2024. The Author(s).)

Purpose: Accurate and rapid needle localization on 3D magnetic resonance imaging (MRI) is critical for MRI-guided percutaneous interventions. The current workflow requires manual needle localization on 3D MRI, which is time-consuming and cumbersome. Automatic methods using 2D deep learning networks for needle segmentation require manual image plane localization, while 3D networks are challenged by the need for sufficient training datasets. This work aimed to develop an automatic deep learning-based pipeline for accurate and rapid 3D needle localization on in vivo intra-procedural 3D MRI using a limited training dataset., Methods: The proposed automatic pipeline adopted Shifted Window (Swin) Transformers and employed a coarse-to-fine segmentation strategy: (1) initial 3D needle feature segmentation with 3D Swin UNEt TRansfomer (UNETR); (2) generation of a 2D reformatted image containing the needle feature; (3) fine 2D needle feature segmentation with 2D Swin Transformer and calculation of 3D needle tip position and axis orientation. Pre-training and data augmentation were performed to improve network training. The pipeline was evaluated via cross-validation with 49 in vivo intra-procedural 3D MR images from preclinical pig experiments. The needle tip and axis localization errors were compared with human intra-reader variation using the Wilcoxon signed rank test, with p < 0.05 considered significant., Results: The average end-to-end computational time for the pipeline was 6 s per 3D volume. The median Dice scores of the 3D Swin UNETR and 2D Swin Transformer in the pipeline were 0.80 and 0.93, respectively. The median 3D needle tip and axis localization errors were 1.48 mm (1.09 pixels) and 0.98°, respectively. Needle tip localization errors were significantly smaller than human intra-reader variation (median 1.70 mm; p < 0.01)., Conclusion: The proposed automatic pipeline achieved rapid pixel-level 3D needle localization on intra-procedural 3D MRI without requiring a large 3D training dataset and has the potential to assist MRI-guided percutaneous interventions., (© 2024. The Author(s).)

Background: The questions of whether the spatial resolution of navigated 3D-ultrasound (3D-US) power-Doppler angiography imaging rendered by existing 3D-US systems is sufficient for the intraoperative visualization of cerebral aneurysms, and in what percentage of cases, are largely unanswered. A study on this topic is lacking in the literature., Methods: From 2015 to 2022, we performed 86 surgeries on 83 aneurysm patients. Navigated 3D-US was used at the discretion of the operating neurosurgeons when available (i.e., not being used during parallel tumor surgeries). Twenty-five aneurysms (15 ruptured) were operated on using 3D-US; 22 aneurysms were located at the middle cerebral artery (MCA). Patient 3D-US power-Doppler angiography images and surgical reports were retrospectively reviewed to assess the intraoperative ultrasound visibility of aneurysms., Results: In 20 patients (80%) the aneurysms were successfully visualized. In five patients (20%), the aneurysms visualization was insufficient or absent. Nineteen of 22 aneurysms (86.4%) were visualized in the MCA aneurysm subgroup. We observed no association between aneurysm visibility and aneurysm size or the presence of subarachnoid hemorrhage. In the subgroup of MCA aneurysms, no association between aneurysm visibility and the presence of subarachnoid hemorrhage was found; a trend toward poor sonographic visibility of smaller aneurysms was observed (p = 0.09)., Conclusions: Our initial data show that intraoperative 3D-US power-Doppler angiography, rendered by current navigated 3D-US systems, clearly depicts the majority of aneurysms in the MCA aneurysm subgroup. However, future prospective studies performed on a higher number of aneurysms localized at various anatomic sites are needed to confirm our initial findings and determine their potential clinical relevance., (© 2024. The Author(s).)

Background: We aimed to develop a framework for generating three-dimensional (3D) myocardial blood flow (MBF) images, computing their accuracy against clinically validated two-dimensional (2D) polar MBF maps of the left ventricle, and evaluating their improvements in image quality over relative myocardial perfusion imaging (MPI)., Methods: N = 40 patients with a wide range of defect severities and uptake dynamics were retrospectively studied. The FlowQuant™ software was used to generate reference MPI and polar MBF maps and was adapted for voxel-wise MBF mapping. We evaluated agreement between parametric vs polar values for MBF at rest and stress and for reserve (stress/rest MBF). We also assessed improvements in image quality, assessed by signal-to-noise ratio, contrast-to-noise ratio, tissue-to-blood ratio, and defect severity, from relative MPI to MBF., Results: There was excellent agreement between 3D parametric and 2D polar maps for all flow parameters (interclass correlation coefficient >0.96), albeit with minimal bias (<8%) for rest and stress MBF at the patient level. Image quality substantially improved from MPI to MBF in every patient for all image-quality metrics (P < 0.0001) CONCLUSIONS: We developed a robust methodology for producing highly accurate 3D MBF images exhibiting considerably improved image quality compared to relative MPI commonly used in clinical practice., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Because of the complex anatomy of the right posterior hepatic pedicle, there have been few reports on standardized laparoscopic portal territory staining-guided anatomical resection of liver segment 6 (LPTAR-S6). This study aimed to elucidate the indocyanine green (ICG) fluorescence staining methods for LPTAR-S6., Patients and Methods: LPTAR-S6 can be performed using positive and negative fluorescence staining approaches. We implemented these two approaches for patients with hepatocellular carcinoma. Descriptions of the surgical strategy and technical details are presented., Results: Two patients safely underwent LPTAR-S6 using a preoperative three-dimensional reconstruction plan. The intraoperative ICG fluorescence staining effect was satisfactory, and the anatomical landmarks were fully exposed., Conclusions: A detailed preoperative three-dimensional reconstruction plan, complete intraoperative application of real-time laparoscopic ultrasound guidance, and ICG fluorescence staining can result in accurate transection of the liver parenchyma during LPTAR-S6., (© 2024. Society of Surgical Oncology.)

Purpose: The accurate and timely assessment of the collateral perfusion status is crucial in the diagnosis and treatment of patients with acute ischemic stroke. Previous works have shown that collateral imaging, derived from CT angiography, MR perfusion, and MR angiography, aids in evaluating the collateral status. However, such methods are time-consuming and/or sub-optimal due to the nature of manual processing and heuristics. Recently, deep learning approaches have shown to be promising for generating collateral imaging. These, however, suffer from the computational complexity and cost., Methods: In this study, we propose a mobile, lightweight deep regression neural network for collateral imaging in acute ischemic stroke, leveraging dynamic susceptibility contrast MR perfusion (DSC-MRP). Built based upon lightweight convolution and Transformer architectures, the proposed model manages the balance between the model complexity and performance., Results: We evaluated the performance of the proposed model in generating the five-phase collateral maps, including arterial, capillary, early venous, late venous, and delayed phases, using DSC-MRP from 952 patients. In comparison with various deep learning models, the proposed method was superior to the competitors with similar complexity and was comparable to the competitors of high complexity., Conclusion: The results suggest that the proposed model is able to facilitate rapid and precise assessment of the collateral status of patients with acute ischemic stroke, leading to improved patient care and outcome., (© 2024. The Author(s).)

Object: In a typical MR session, several contrasts are acquired. Due to the sequential nature of the data acquisition process, the patient may experience some discomfort at some point during the session, and start moving. Hence, it is quite common to have MR sessions where some contrasts are well-resolved, while other contrasts exhibit motion artifacts. Instead of repeating the scans that are corrupted by motion, we introduce a reference-guided retrospective motion correction scheme that takes advantage of the motion-free scans, based on a generalized rigid registration routine., Materials and Methods: We focus on various existing clinical 3D brain protocols at 1.5 Tesla MRI based on Cartesian sampling. Controlled experiments with three healthy volunteers and three levels of motion are performed., Results: Radiological inspection confirms that the proposed method consistently ameliorates the corrupted scans. Furthermore, for the set of specific motion tests performed in this study, the quality indexes based on PSNR and SSIM shows only a modest decrease in correction quality as a function of motion complexity., Discussion: While the results on controlled experiments are positive, future applications to patient data will ultimately clarify whether the proposed correction scheme satisfies the radiological requirements., (© 2024. The Author(s).)

In clinical practice, the morphology of the left atrial appendage (LAA) plays an important role in the selection of LAA closure devices for LAA closure procedures. The morphology determination is influenced by the segmentation results. The LAA occupies only a small part of the entire 3D medical image, and the segmentation results are more likely to be biased towards the background region, making the segmentation of the LAA challenging. In this paper, we propose a lightweight attention mechanism called fusion attention, which imitates human visual behavior. We process the 3D image of the LAA using a method that involves overview observation followed by detailed observation. In the overview observation stage, the image features are pooled along the three dimensions of length, width, and height. The obtained features from the three dimensions are then separately input into the spatial attention and channel attention modules to learn the regions of interest. In the detailed observation stage, the attention results from the previous stage are fused using element-wise multiplication and combined with the original feature map to enhance feature learning. The fusion attention mechanism was evaluated on a left atrial appendage dataset provided by Liaoning Provincial People's Hospital, resulting in an average Dice coefficient of 0.8855. The results indicate that the fusion attention mechanism achieves better segmentation results on 3D images compared to existing lightweight attention mechanisms., (© 2024. International Federation for Medical and Biological Engineering.)

Blood velocities measured by Transcranial Doppler (TCD) are dependent on the angle between the incident ultrasound beam and the direction of blood flow (known as the Doppler angle). However, when TCD examinations are performed without imaging the Doppler angle for each vessel segment is not known. We have measured Doppler angles in the basal cerebral arteries examined with TCD using three-dimensional (3D) vessel models generated from computed tomography angiography (CTA) scans. This approach produces angle statistics that are not accessible during non-imaging TCD studies. We created 3D models of the basal cerebral arteries for 24 vasospasm patients. Standard acoustic windows were mapped to the specific anatomy of each patient. Virtual ultrasound transmit beams were generated that originated from the acoustic window and intersected the centerline of each arterial segment. Doppler angle measurements were calculated and compiled for each vessel segment. Doppler angles were smallest for the middle cerebral artery M1 segment (median 24.6°) and ophthalmic artery (median 25.0°), and largest for the anterior cerebral artery A2 segment (median 76.4°) and posterior cerebral artery P2 segment (median 75.8°). The ophthalmic artery had the highest proportion of Doppler angles that were less than 60° (99%) while the anterior cerebral artery A2 segment had the lowest proportion of Doppler angles that were less than 60° (10%). These angle measurements indicate the expected deviation between measured and true velocities in the cerebral arteries, highlighting specific segments that may be prone to underestimation of velocity., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

Purpose: Robot-assisted systems offer an opportunity to support the diagnostic and therapeutic treatment of vascular diseases to reduce radiation exposure and support the limited medical staff in vascular medicine. In the diagnosis and follow-up care of vascular pathologies, Doppler ultrasound has become the preferred diagnostic tool. The study presents a robotic system for automatic Doppler ultrasound examinations of patients' leg vessels., Methods: The robotic system consists of a redundant 7 DoF serial manipulator, to which a 3D ultrasound probe is attached. A compliant control was employed, whereby the transducer was guided along the vessel with a defined contact force. Visual servoing was used to correct the position of the probe during the scan so that the vessel can always be properly visualized. To track the vessel's position, methods based on template matching and Doppler sonography were used., Results: Our system was able to successfully scan the femoral artery of seven volunteers automatically for a distance of 20 cm. In particular, our approach using Doppler ultrasound data showed high robustness and an accuracy of 10.7 (±3.1) px in determining the vessel's position and thus outperformed our template matching approach, whereby an accuracy of 13.9 (±6.4) px was achieved., Conclusions: The developed system enables automated robotic ultrasound examinations of vessels and thus represents an opportunity to reduce radiation exposure and staff workload. The integration of Doppler ultrasound improves the accuracy and robustness of vessel tracking, and could thus contribute to the realization of routine robotic vascular examinations and potential endovascular interventions., (© 2024. The Author(s).)

Three-dimensional vessel model reconstruction from patient-specific magnetic resonance angiography (MRA) images often requires some manual maneuvers. This study aimed to establish the deep learning (DL)-based method for vessel model reconstruction. Time of flight MRA of 40 patients with internal carotid artery aneurysms was prepared, and three-dimensional vessel models were constructed using the threshold and region-growing method. Using those datasets, supervised deep learning using 2D U-net was performed to reconstruct 3D vessel models. The accuracy of the DL-based vessel segmentations was assessed using 20 MRA images outside the training dataset. The dice coefficient was used as the indicator of the model accuracy, and the blood flow simulation was performed using the DL-based vessel model. The created DL model could successfully reconstruct a three-dimensional model in all 60 cases. The dice coefficient in the test dataset was 0.859. Of note, the DL-generated model proved its efficacy even for large aneurysms (> 10 mm in their diameter). The reconstructed model was feasible in performing blood flow simulation to assist clinical decision-making. Our DL-based method could successfully reconstruct a three-dimensional vessel model with moderate accuracy. Future studies are warranted to exhibit that DL-based technology can promote medical image processing., (© 2024. The Author(s).)

Purpose: Adolescent idiopathic scoliosis is a chronic disease that may require correction surgery. The finite element method (FEM) is a popular option to plan the outcome of surgery on a patient-based model. However, it requires considerable computing power and time, which may discourage its use. Machine learning (ML) models can be a helpful surrogate to the FEM, providing accurate real-time responses. This work implements ML algorithms to estimate post-operative spinal shapes., Methods: The algorithms are trained using features from 6400 simulations generated using the FEM from spine geometries of 64 patients. The features are selected using an autoencoder and principal component analysis. The accuracy of the results is evaluated by calculating the root-mean-squared error and the angle between the reference and predicted position of each vertebra. The processing times are also reported., Results: A combination of principal component analysis for dimensionality reduction, followed by the linear regression model, generated accurate results in real-time, with an average position error of 3.75 mm and orientation angle error below 2.74 degrees in all main 3D axes, within 3 ms. The prediction time is considerably faster than simulations based on the FEM alone, which require seconds to minutes., Conclusion: It is possible to predict post-operative spinal shapes of patients with AIS in real-time by using ML algorithms as a surrogate to the FEM. Clinicians can compare the response of the initial spine shape of a patient with AIS to various target shapes, which can be modified interactively. These benefits can encourage clinicians to use software tools for surgical planning of scoliosis., (© 2024. Crown.)

Objective: To prepare and analyze soy-lecithin-agar gels for non-toxic relaxometry phantoms with tissue-like relaxation times at 3T., Methods: Phantoms mimicking the relaxation times of various tissues (gray and white matter, kidney cortex and medulla, spleen, muscle, liver) were built and tested with a clinical 3T whole-body MR scanner. Simple equations were derived to calculate the appropriate concentrations of soy lecithin and agar in aqueous solutions to achieve the desired relaxation times. Phantoms were tested for correspondence between measurements and calculated T1 and T2 values, reproducibility, spatial homogeneity, and temporal stability. T1 and T2 mapping techniques and a 3D T1-weighted sequence with high spatial resolution were applied., Results: Except for the liver relaxation phantom, all phantoms were successfully and reproducibly produced. Good agreement was found between the targeted and measured relaxation times. The percentage deviations from the targeted relaxation times were less than 3% for T1 and less than 6.5% for T2. In addition, the phantoms were homogeneous and had little to no air bubbles. However, the phantoms were unstable over time: after a storage period of 4 weeks, mold growth and also changes in relaxation times were detected in almost all phantoms., Conclusion: Soy-lecithin-agar gels are a non-toxic material for the construction of relaxometry phantoms with tissue-like relaxation times. They are easy to prepare, inexpensive and allow independent adjustment of T1 and T2. However, there is still work to be done to improve the long-term stability of the phantoms., (© 2024. The Author(s).)

Purpose: Differentiating pulmonary lymphoma from lung infections using CT images is challenging. Existing deep neural network-based lung CT classification models rely on 2D slices, lacking comprehensive information and requiring manual selection. 3D models that involve chunking compromise image information and struggle with parameter reduction, limiting performance. These limitations must be addressed to improve accuracy and practicality., Methods: We propose a transformer sequential feature encoding structure to integrate multi-level information from complete CT images, inspired by the clinical practice of using a sequence of cross-sectional slices for diagnosis. We incorporate position encoding and cross-level long-range information fusion modules into the feature extraction CNN network for cross-sectional slices, ensuring high-precision feature extraction., Results: We conducted comprehensive experiments on a dataset of 124 patients, with respective sizes of 64, 20 and 40 for training, validation and testing. The results of ablation experiments and comparative experiments demonstrated the effectiveness of our approach. Our method outperforms existing state-of-the-art methods in the 3D CT image classification problem of distinguishing between lung infections and pulmonary lymphoma, achieving an accuracy of 0.875, AUC of 0.953 and F1 score of 0.889., Conclusion: The experiments verified that our proposed position-enhanced transformer-based sequential feature encoding model is capable of effectively performing high-precision feature extraction and contextual feature fusion in the lungs. It enhances the ability of a standalone CNN network or transformer to extract features, thereby improving the classification performance. The source code is accessible at https://github.com/imchuyu/PTSFE ., (© 2024. CARS.)

Purpose: Clinical needle insertion into tissue, commonly assisted by 2D ultrasound imaging for real-time navigation, faces the challenge of precise needle and probe alignment to reduce out-of-plane movement. Recent studies investigate 3D ultrasound imaging together with deep learning to overcome this problem, focusing on acquiring high-resolution images to create optimal conditions for needle tip detection. However, high-resolution also requires a lot of time for image acquisition and processing, which limits the real-time capability. Therefore, we aim to maximize the US volume rate with the trade-off of low image resolution. We propose a deep learning approach to directly extract the 3D needle tip position from sparsely sampled US volumes., Methods: We design an experimental setup with a robot inserting a needle into water and chicken liver tissue. In contrast to manual annotation, we assess the needle tip position from the known robot pose. During insertion, we acquire a large data set of low-resolution volumes using a 16  ×  16 element matrix transducer with a volume rate of 4 Hz. We compare the performance of our deep learning approach with conventional needle segmentation., Results: Our experiments in water and liver show that deep learning outperforms the conventional approach while achieving sub-millimeter accuracy. We achieve mean position errors of 0.54 mm in water and 1.54 mm in liver for deep learning., Conclusion: Our study underlines the strengths of deep learning to predict the 3D needle positions from low-resolution ultrasound volumes. This is an important milestone for real-time needle navigation, simplifying the alignment of needle and ultrasound probe and enabling a 3D motion analysis., (© 2024. The Author(s).)

Magnetic resonance imaging (MRI) is a crucial modality for abdominal imaging evaluation of focal lesions and tissue properties. However, several obstacles, such as prolonged scan times, limitations in patients' breath-hold capacity, and contrast agent-associated artifacts, remain in abdominal MR images. Recent techniques, including parallel imaging, three-dimensional acquisition, compressed sensing, and deep learning, have been developed to reduce the scan time while ensuring acceptable image quality or to achieve higher resolution without extending the scan duration. Quantitative measurements using MRI techniques enable the noninvasive evaluation of specific materials. A comprehensive understanding of these advanced techniques is essential for accurate interpretation of MRI sequences. Herein, we therefore review advanced abdominal MRI techniques., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: The current study explores the application of 3D U-Net architectures combined with Inception and ResNet modules for precise lung nodule detection through deep learning-based segmentation technique. This investigation is motivated by the objective of developing a Computer-Aided Diagnosis (CAD) system for effective diagnosis and prognostication of lung nodules in clinical settings., Methods: The proposed method trained four different 3D U-Net models on the retrospective dataset obtained from AIIMS Delhi. To augment the training dataset, affine transformations and intensity transforms were utilized. Preprocessing steps included CT scan voxel resampling, intensity normalization, and lung parenchyma segmentation. Model optimization utilized a hybrid loss function that combined Dice Loss and Focal Loss. The model performance of all four 3D U-Nets was evaluated patient-wise using dice coefficient and Jaccard coefficient, then averaged to obtain the average volumetric dice coefficient (DSC avg ) and average Jaccard coefficient (IoU avg ) on a test dataset comprising 53 CT scans. Additionally, an ensemble approach (Model-V) was utilized featuring 3D U-Net (Model-I), ResNet (Model-II), and Inception (Model-III) 3D U-Net architectures, combined with two distinct patch sizes for further investigation., Results: The ensemble of models obtained the highest DSC avg of 0.84 ± 0.05 and IoU avg of 0.74 ± 0.06 on the test dataset, compared against individual models. It mitigated false positives, overestimations, and underestimations observed in individual U-Net models. Moreover, the ensemble of models reduced average false positives per scan in the test dataset (1.57 nodules/scan) compared to individual models (2.69-3.39 nodules/scan)., Conclusions: The suggested ensemble approach presents a strong and effective strategy for automatically detecting and delineating lung nodules, potentially aiding CAD systems in clinical settings. This approach could assist radiologists in laborious and meticulous lung nodule detection tasks in CT scans, improving lung cancer diagnosis and treatment planning., (© 2024. CARS.)

Background: Detection and localization of cerebral microbleeds (CMBs) is crucial for disease diagnosis and treatment planning. However, CMB detection is labor-intensive, time-consuming, and challenging owing to its visual similarity to mimics. This study aimed to validate the performance of a three-dimensional (3D) deep learning model that not only detects CMBs but also identifies their anatomic location in real-world settings., Methods: A total of 21 patients with 116 CMBs and 12 without CMBs were visited in the neurosurgery outpatient department between January 2023 and October 2023. Three readers, including a board-certified neuroradiologist (reader 1), a resident in radiology (reader 2), and a neurosurgeon (reader 3) independently reviewed SWIs of 33 patients to detect CMBs and categorized their locations into lobar, deep, and infratentorial regions without any AI assistance. After a one-month washout period, the same datasets were redistributed randomly, and readers reviewed them again with the assistance of the 3D deep learning model. A comparison of the diagnostic performance between readers with and without AI assistance was performed., Results: All readers with an AI assistant (reader 1:0.991 [0.930-0.999], reader 2:0.922 [0.881-0.905], and reader 3:0.966 [0.928-0.984]) tended to have higher sensitivity per lesion than readers only (reader 1:0.905 [0.849-0.942], reader 2:0.621 [0.541-0.694], and reader 3:0.871 [0.759-0.935], p = 0.132, 0.017, and 0.227, respectively). In particular, radiology residents (reader 2) showed a statistically significant increase in sensitivity per lesion when using AI. There was no statistically significant difference in the number of FPs per patient for all readers with AI assistant (reader 1: 0.394 [0.152-1.021], reader 2: 0.727 [0.334-1.582], reader 3: 0.182 [0.077-0.429]) and reader only (reader 1: 0.364 [0.159-0.831], reader 2: 0.576 [0.240-1.382], reader 3: 0.121 [0.038-0.383], p = 0.853, 0.251, and 0.157, respectively). Our model accurately categorized the anatomical location of all CMBs., Conclusions: Our model demonstrated promising potential for the detection and anatomical localization of CMBs, although further research with a larger and more diverse population is necessary to establish clinical utility in real-world settings., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.)

Objectives: A deep knowledge of the surgical anatomy of the target area is mandatory for a successful operative procedure. For this purpose, over the years, many teaching and learning methods have been described, from the most ancient cadaveric dissection to the most recent virtual reality, each with their respective pros and cons. Photogrammetry, an emergent technique, allows for the creation of three-dimensional (3D) models and reconstructions. Thanks to the spreading of photogrammetry nowadays it is possible to generate these models using professional software or even smartphone apps. This study aims to compare the neuroanatomical photogrammetric models generated by the two most utilized smartphone applications in this domain, Metascan and 3D-Scanner, through quantitative analysis., Methods: Two human head specimens (four sides) were examined. Anatomical dissection was segmented into five stages to systematically expose well-defined structures. After each stage, a photogrammetric model was generated using two prominent smartphone applications. These models were then subjected to both quantitative and qualitative analysis, with a specific focus on comparing the mesh density as a measure of model resolution and accuracy. Appropriate consent was obtained for the publication of the cadaver's image., Results: The quantitative analysis revealed that the models generated by Metascan app consistently demonstrated superior mesh density compared to those from 3D-Scanner, indicating a higher level of detail and potential for precise anatomical representation., Conclusion: Enabling depth perception, capturing high-quality images, offering flexibility in viewpoints: photogrammetry provides researchers with unprecedented opportunities to explore and understand the intricate and magnificent structure of the brain. However, it is of paramount importance to develop and apply rigorous quality control systems to ensure data integrity and reliability of findings in neurological research. This study has demonstrated the superiority of Metascan in processing photogrammetric models for neuroanatomical studies., (© 2024. The Author(s).)

Purpose: Instrumentation of the C1 vertebra requires either mobilization or transection of the C2 nerve root. This study investigates clinical and radiological outcomes and incidences of C2 neuropathic pain after posterior instrumented fusion in the cranio-cervical junction with or without division of the C2 nerve roots., Methods: This retrospective study compared two cohorts of patients who underwent instrumented fusion in the cranio-cervical junction. Fifty patients (22 males and 28 females) were operated with complete resection of C2 nerve root ganglion (Ex group), and fifty-one patients (30 men, 21 women) with C2 nerve roots preservation (No group)., Results: The incidence of postoperative C2 neuropathy was eight times lower in the Ex group compared to the No group that was statistical significant, p = 0.039. Surgical time was significantly shorter in the No group (p = 0.001). The fusion rates were very high for both groups, without difference between groups (p = 1.0). Autografting from the iliac crest (p = 0.001) as well as postoperative immobilisation with a hard collar (p < 0.001) were required in fewer patients in the Ex group. Also, patients in the Ex group were mobilised faster after surgery (p = 0.49). Overall, complication rates were similar between groups, but the Ex group demonstrated fewer major medical complications (16% vs 31%). Male sex and iliac bone harvesting demonstrated significantly higher OR for development of postoperative complications (p = 0.023 and p = 0.034 respectively) and postoperative mobilization demonstrated significant higher OR for development of postoperative major complications (p = 0.042)., Conclusions: Resection of the C2 nerve root ganglion during posterior instrumented fusion of the cranio-cervical junction is safe and rarely leads to C2 neuropathy. The technique tends to mitigate the odds of developing postoperative complications., (© 2024. The Author(s).)

Purpose: Recent innovations in radiological imaging have enabled the detection of micro-remnant niduses of arteriovenous malformations (AVMs) after gamma knife radiosurgery (GKS), which have not been previously perceptible. Herein, we focus on the difficulty of evaluating micro-remnant AVMs after GKS that are hardly perceptible on conventional examinations and propose integrating follow-up three-dimensional rotational angiography (3D-RA) in the previous gamma plan as a solution., Methods: We retrospectively searched NTT Medical Center Tokyo hospital database for patients with AVMs who underwent both two-dimensional digital subtraction angiography (2D-DSA) and 3D-RA as follow-up for GKS from February 2021 to January 2024. Patients with suspected nidus occlusion on the latest non-contrast-enhanced magnetic resonance angiography (NC-MRA) were included, and contrast-enhanced magnetic resonance angiography (CE-MRA), 2D-DSA, and 3D-RA were evaluated., Results: Twelve patients with 13 AVM sites were defined as having complete nidus occlusion on upfront NC-MRA. On 2D-DSA, seven AVM sites showed the presence of slight remaining AVMs based on the detection of remnant drainage veins, however the nidus was not detected in three cases. Nevertheless, 3D-RA detected micro-remnant niduses in all seven AVM sites, and four patients underwent re-GKS. Nine patients with ten AVM sites also underwent CE-MRA, and six AVM sites were diagnosed with radiation-induced parenchymal injury., Conclusion: Importing the 3D-RA image into the treatment planning has the potential to be more helpful than NC-MRA or CE-MRA to detect micro-remnant AVMs and evaluate the true remnant volume, and may contribute to a more detailed treatment planning, thereby improving the results of GKS retreatment., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.)

Purpose: Three-dimensional (3D) preoperative planning has become the gold standard for orthopedic surgeries, primarily relying on CT-reconstructed 3D models. However, in contrast to standing radiographs, a CT scan is not part of the standard protocol but is usually acquired for preoperative planning purposes only. Additionally, it is costly, exposes the patients to high doses of radiation and is acquired in a non-weight-bearing position., Methods: In this study, we develop a deep-learning based pipeline to facilitate 3D preoperative planning for high tibial osteotomies, based on 3D models reconstructed from low-dose biplanar standing EOS radiographs. Using digitally reconstructed radiographs, we train networks to localize the clinically required landmarks, separate the two legs in the sagittal radiograph and finally reconstruct the 3D bone model. Finally, we evaluate the accuracy of the reconstructed 3D models for the particular application case of preoperative planning, with the aim of eliminating the need for a CT scan in specific cases, such as high tibial osteotomies., Results: The mean Dice coefficients for the tibial reconstructions were 0.92 and 0.89 for the right and left tibia, respectively. The reconstructed models were successfully used for clinical-grade preoperative planning in a real patient series of 52 cases. The mean differences to ground truth values for mechanical axis and tibial slope were 0.52° and 4.33°, respectively., Conclusions: We contribute a novel framework for the 2D-3D reconstruction of bone models from biplanar standing EOS radiographs and successfully use them in automated clinical-grade preoperative planning of high tibial osteotomies. However, achieving precise reconstruction and automated measurement of tibial slope remains a significant challenge., (© 2024. The Author(s).)

Purpose: Paranasal anomalies, frequently identified in routine radiological screenings, exhibit diverse morphological characteristics. Due to the diversity of anomalies, supervised learning methods require large labelled dataset exhibiting diverse anomaly morphology. Self-supervised learning (SSL) can be used to learn representations from unlabelled data. However, there are no SSL methods designed for the downstream task of classifying paranasal anomalies in the maxillary sinus (MS)., Methods: Our approach uses a 3D convolutional autoencoder (CAE) trained in an unsupervised anomaly detection (UAD) framework. Initially, we train the 3D CAE to reduce reconstruction errors when reconstructing normal maxillary sinus (MS) image. Then, this CAE is applied to an unlabelled dataset to generate coarse anomaly locations by creating residual MS images. Following this, a 3D convolutional neural network (CNN) reconstructs these residual images, which forms our SSL task. Lastly, we fine-tune the encoder part of the 3D CNN on a labelled dataset of normal and anomalous MS images., Results: The proposed SSL technique exhibits superior performance compared to existing generic self-supervised methods, especially in scenarios with limited annotated data. When trained on just 10% of the annotated dataset, our method achieves an area under the precision-recall curve (AUPRC) of 0.79 for the downstream classification task. This performance surpasses other methods, with BYOL attaining an AUPRC of 0.75, SimSiam at 0.74, SimCLR at 0.73 and masked autoencoding using SparK at 0.75., Conclusion: A self-supervised learning approach that inherently focuses on localizing paranasal anomalies proves to be advantageous, particularly when the subsequent task involves differentiating normal from anomalous maxillary sinuses. Access our code at https://github.com/mtec-tuhh/self-supervised-paranasal-anomaly ., (© 2024. The Author(s).)

Purpose: MRI-derived brain volume loss (BVL) is widely used as neurodegeneration marker. SIENA is state-of-the-art for BVL measurement, but limited by long computation time. Here we propose "BrainLossNet", a convolutional neural network (CNN)-based method for BVL-estimation., Methods: BrainLossNet uses CNN-based non-linear registration of baseline(BL)/follow-up(FU) 3D-T1w-MRI pairs. BVL is computed by non-linear registration of brain parenchyma masks segmented in the BL/FU scans. The BVL estimate is corrected for image distortions using the apparent volume change of the total intracranial volume. BrainLossNet was trained on 1525 BL/FU pairs from 83 scanners. Agreement between BrainLossNet and SIENA was assessed in 225 BL/FU pairs from 94 MS patients acquired with a single scanner and 268 BL/FU pairs from 52 scanners acquired for various indications. Robustness to short-term variability of 3D-T1w-MRI was compared in 354 BL/FU pairs from a single healthy men acquired in the same session without repositioning with 116 scanners (Frequently-Traveling-Human-Phantom dataset, FTHP)., Results: Processing time of BrainLossNet was 2-3 min. The median [interquartile range] of the SIENA-BrainLossNet BVL difference was 0.10% [- 0.18%, 0.35%] in the MS dataset, 0.08% [- 0.14%, 0.28%] in the various indications dataset. The distribution of apparent BVL in the FTHP dataset was narrower with BrainLossNet (p = 0.036; 95th percentile: 0.20% vs 0.32%)., Conclusion: BrainLossNet on average provides the same BVL estimates as SIENA, but it is significantly more robust, probably due to its built-in distortion correction. Processing time of 2-3 min makes BrainLossNet suitable for clinical routine. This can pave the way for widespread clinical use of BVL estimation from intra-scanner BL/FU pairs., (© 2024. The Author(s).)

Purpose: The conventional method to reconstruct the bone level for orbital defects, which is based on mirroring and manual adaptation, is time-consuming and the accuracy highly depends on the expertise of the clinical engineer. The aim of this study is to propose and evaluate an automated reconstruction method utilizing a Gaussian process morphable model (GPMM)., Methods: Sixty-five Computed Tomography (CT) scans of healthy midfaces were used to create a GPMM that can model shape variations of the orbital region. Parameter optimization was performed by evaluating several quantitative metrics inspired on the shape modeling literature, e.g. generalization and specificity. The reconstruction error was estimated by reconstructing artificial defects created in orbits from fifteen CT scans that were not included in the GPMM. The developed algorithms utilize the existing framework of Gaussian process morphable models, as implemented in the Scalismo software., Results: By evaluating the proposed quality metrics, adequate parameters are chosen for non-rigid registration and reconstruction. The resulting median reconstruction error using the GPMM was lower (0.35 ± 0.16 mm) compared to the mirroring method (0.52 ± 0.18 mm). In addition, the GPMM-based reconstruction is automated and can be applied to large bilateral defects with a median reconstruction error of 0.39 ± 0.11 mm., Conclusion: The GPMM-based reconstruction proves to be less time-consuming and more accurate than reconstruction by mirroring. Further validation through clinical studies on patients with orbital defects is warranted. Nevertheless, the results underscore the potential of GPMM-based reconstruction as a promising alternative for designing patient-specific implants., (© 2024. CARS.)

Purpose: Many artifacts and obstacles associated with cone-beam computed tomography (CBCT) scan can obscure or distort the details of the teeth and occlusal surface, like distorted teeth, streak artifacts, noise, and some malocclusion cases with excessive overlapping between jaws cause decrease the interocclusal space, which can impact diagnosis and treatment planning, and the 3D reconstruction accuracy. Optimizing dental precision by Integrating CBCT scans with other imaging modalities, supply more information to enhance CBCT accuracy, mainly in dental areas with limited clarity., Methods: Performing the Structure-from-Motion (SfM) photogrammetry method, using phone camera and photograph studio setup using simple hardware, to digitize the dental casts and obtain an accurate digital dental model. Using this digital dental model to enhance dental precision in the CBCT data by performing the superimposition process, using a surface-based registration method and integration process to create a virtual dentoskeletal model. Evaluate the accuracy and quality of the superimposition results using qualitative (visual inspection) and quantitative measures., Results: The differences between the virtual dentoskeletal model and the reference CBCT model are calculating by the 3D Euclidean distance, the mean ± SD are 0.212 ± 0.169 mm and 0.26 ± 0.149 mm for the maxilla and mandible, respectively. The color-coded map shows that the two surfaces are similar, but the extremist values are concentrated in the dental region due to the presence of the noise in the reference model and the gingiva in the virtual dentoskeletal model., Conclusions: The resulting virtual dentoskeletal model can be viewed and manipulated on a computer screen, allowing for a detailed analysis of the teeth and supporting structures. The 3D model generated by the SfM photogrammetry technique did well during the superimposition process, representing a reliable method for virtual-based processing such as orthognathic surgery planning and splint design., (© 2024. CARS.)

Objectives: In patients having naïve glioblastoma multiforme (GBM), this study aims to assess the efficacy of Deep Learning algorithms in automating the segmentation of brain magnetic resonance (MR) images to accurately determine 3D masks for 4 distinct regions: enhanced tumor, peritumoral edema, non-enhanced/necrotic tumor, and total tumor., Material and Methods: A 3D U-Net neural network algorithm was developed for semantic segmentation of GBM. The training dataset was manually delineated by a group of expert neuroradiologists on MR images from the Brain Tumor Segmentation Challenge 2021 (BraTS2021) image repository, as ground truth labels for diverse glioma (GBM and low-grade glioma) subregions across four MR sequences (T1w, T1w-contrast enhanced, T2w, and FLAIR) in 1251 patients. The in-house test was performed on 50 GBM patients from our cohort (PerProGlio project). By exploring various hyperparameters, the network's performance was optimized, and the most optimal parameter configuration was identified. The assessment of the optimized network's performance utilized Dice scores, precision, and sensitivity metrics., Results: Our adaptation of the 3D U-net with additional residual blocks demonstrated reliable performance on both the BraTS2021 dataset and the in-house PerProGlio cohort, employing only T1w-ce sequences for enhancement and non-enhanced/necrotic tumor models and T1w-ce + T2w + FLAIR for peritumoral edema and total tumor. The mean Dice scores (training and test) were 0.89 and 0.75; 0.75 and 0.64; 0.79 and 0.71; and 0.60 and 0.55, for total tumor, edema, enhanced tumor, and non-enhanced/necrotic tumor, respectively., Conclusions: The results underscore the high precision with which our network can effectively segment GBM tumors and their distinct subregions. The level of accuracy achieved agrees with the coefficients recorded in previous GBM studies. In particular, our approach allows model specialization for each of the different tumor subregions employing only those MR sequences that provide value for segmentation., (© 2024. CARS.)

Purpose: Intracranial aneurysm detection from 3D Time-Of-Flight Magnetic Resonance Angiography images is a problem of increasing clinical importance. Recently, a streak of methods have shown promising performance by using segmentation neural networks. However, these methods may be less relevant in a clinical settings where diagnostic decisions rely on detecting objects rather than their segmentation., Methods: We introduce a 3D single-stage object detection method tailored for small object detection such as aneurysms. Our anchor-free method incorporates fast data annotation, adapted data sampling and generation to address class imbalance problem, and spherical representations for improved detection., Results: A comprehensive evaluation was conducted, comparing our method with the state-of-the-art SCPM-Net, nnDetection and nnUNet baselines, using two datasets comprising 402 subjects. The evaluation used adapted object detection metrics. Our method exhibited comparable or superior performance, with an average precision of 78.96%, sensitivity of 86.78%, and 0.53 false positives per case., Conclusion: Our method significantly reduces the detection complexity compared to existing methods and highlights the advantages of object detection over segmentation-based approaches for aneurysm detection. It also holds potential for application to other small object detection problems., (© 2024. CARS.)

Purpose: Preoperative planning of maxillary anterior dental implant is a prerequisite to ensuring that the implant achieves the proper three-dimensional (3D) pose, which is essential for its long-term stability. However, the current planning process is labor-intensive and subjective, relying heavily on the surgeon's experience. Consequently, this paper proposes an automatic method for computing the optimal pose of the dental implant., Methods: The method adopts the principle of prosthetically guided dental implant placement. Initially, the prosthesis coordinate system is established to determine the implant candidate orientations. Subsequently, virtual slices of the maxilla in the buccal-palatal direction are generated according to the prosthesis position. By extracting feature points from the virtual slices, the implant candidate starting points are acquired. Then, a candidate pose set is obtained by combining these candidate starting points and orientations. Finally, a pose evaluation indicator is introduced to determine the optimal implant pose from this set., Results: Twenty-two cases were utilized to validate the method. The results show that the method could determine an ideal pose for the dental implant, with the average minimum distance between the implant and the left tooth root, the right tooth root, the palatal side, and the buccal side being 2.57 ± 0.53 mm, 2.59 ± 0.65 mm, 0.74 ± 0.19 mm, 1.83 ± 0.16 mm, respectively. The planning time was less than 9 s., Conclusion: Unlike manual planning, the proposed method can efficiently and accurately complete maxillary anterior dental implant planning, providing a theoretical analysis of the success rate of the implant. Thus, it has great potential for future clinical application., (© 2024. CARS.)

Background: Bladder cancer treatment decisions hinge on detecting muscle invasion. The 2018 "Vesical Imaging Reporting and Data System" (VI-RADS) standardizes multiparametric MRI (mp-MRI) use. Radiomics, an analysis framework, provides more insightful information than conventional methods., Purpose: To determine how well MIBC (Muscle Invasive Bladder Cancer) and NMIBC (Non-Muscle Invasive Bladder Cancer) can be distinguished using mp-MRI radiomics features., Methods: We conducted a study with 73 bladder cancer patients diagnosed pathologically, who underwent preoperative mp-MRI from January 2020 to July 2022. Utilizing 3D Slicer (version 4.8.1) and Pyradiomics, we manually extracted radiomic features from apparent diffusion coefficient (ADC) maps created from diffusion-weighted imaging. The LASSO approach identified optimal features, and we addressed sample imbalance using SMOTE. We developed a classification model using textural features alone or combined with VI-RADS, employing a random forest classifier with 10-fold cross-validation. Diagnostic performance was assessed using the area under the ROC curve analysis., Results: Among 73 patients (63 men, 10 women; median age: 63 years), 41 had muscle-invasive and 32 had superficial bladder cancer. Muscle invasion was observed in 25 of 41 patients with VI-RADS 4 and 5 scores and 12 of 32 patients with VI-RADS 1, 2, and 3 scores (accuracy: 77.5%, sensitivity: 67.7%, specificity: 88.8%). The combined VI-RADS score and radiomics model (AUC = 0.92 ± 0.12) outperformed the single radiomics model using ADC MRI (AUC = 0.83 ± 0.22 with 10-fold cross-validation) in this dataset., Conclusion: Before undergoing surgery, bladder cancer invasion in muscle might potentially be predicted using a radiomics signature based on mp-MRI., (© 2024. Society of Surgical Oncology.)

Purpose: Since 2011, we have used computed tomography (CT)-based navigation to perform safe and accurate rotational acetabular osteotomy (RAO) for treating developmental dysplasia of the hip. We developed a new method with four fiducial points to improve the accuracy of a published technique. In this study, we introduced a new method to achieve reorientation in accordance with planning and evaluated its accuracy., Methods: This study included 40 joints, which underwent RAO used CT-based navigation. In 20 joints, reorientation was confirmed by touching the lateral aspect of the rotated fragment with navigation and checking whether it matched the preoperative plan. A new fiducial point method was adopted for the remaining 20 joints. To assess the accuracy of the position of the rotated fragment in each group, postoperative radial reformatted CT images were obtained around the acetabulum and three-dimensional evaluation was performed. The accuracy of acetabular fragment repositioning was evaluated using the acetabular sector angle (ASA)., Results: The absolute value of ΔASA, which represents the error between preoperative planning and the actual postoperative position, was significantly smaller in the new fiducial method group than the previous method group in the area from 11:30 to 13:30 (p < 0.05). The Harris Hip Score at 1 year after surgery did not differ significantly between the previous and new fiducial point methods., Conclusion: The new fiducial point method significantly reduced reorientation error in the superior-lateral area of the acetabulum: significantly fewer errors and fewer cases of under-correction of lateral acetabular coverage were recorded. The four-reference fiducial method facilitates reorientation of the acetabulum as planned, with fewer errors. The effect of the improved accuracy of the fiducial point method on clinical outcomes will be investigated in the future work., (© 2024. The Author(s).)

Background: Obstetric anal injury is the main risk factor for traumatic anal, faecal and flatus, incontinence in women in reproductive age. Its recognition and good reparation are crucial for long term outcomes., Case Report: We report a case of a nulliparous woman who reported a fourth-degree perineal tear after delivery. The obstetric anal sphincter injury was repaired and a four-dimensional transperineal ultrasound was performed after reparation and then one and three months after discharge. The woman did not experience any incontinence and no points of discontinuity were observed at tomographic ultrasound imaging reconstruction. In this case, 4D-TPUS was a tool in the obstetrician's hands to evaluate the surgical success of OASIS repair, which requires some expertise., Conclusions: 4D-TPUS ultrasound is useful and reliable during immediate puerperium and in the subsequent follow-up, giving positive feedback on the correct positioning of the stiches and on a good healing process. Further studies are needed to demonstrate this usefulness in the operators training and for improving their surgical skills., (© 2024. Società Italiana di Ultrasonologia in Medicina e Biologia (SIUMB).)

Purpose: To develop a new MR coronary angiography (MRCA) technique by employing a zigzag fan-shaped centric k y -k z k-space trajectory combined with high-resolution deep learning reconstruction (HR-DLR)., Methods: All imaging data were acquired from 12 healthy subjects and 2 patients using two clinical 3-T MR imagers, with institutional review board approval. Ten healthy subjects underwent both standard 3D fast gradient echo (sFGE) and centric ky-kz k-space trajectory FGE (cFGE) acquisitions to compare the scan time and image quality. Quantitative measures were also performed for signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) as well as sharpness of the vessel. Furthermore, the feasibility of the proposed cFGE sequence was assessed in two patients. For assessing the feasibility of the centric k y -k z trajectory, the navigator-echo window of a 30-mm threshold was applied in cFGE, whereas sFGE was applied using a standard 5-mm threshold. Image quality of MRCA using cFGE with HR-DLR and sFGE without HR-DLR was scored in a 5-point scale (non-diagnostic = 1, fair = 2, moderate = 3, good = 4, and excellent = 5). Image evaluation of cFGE, applying HR-DLR, was compared with sFGE without HR-DLR. Friedman test, Wilcoxon signed-rank test, or paired t tests were performed for the comparison of related variables., Results: The actual MRCA scan time of cFGE with a 30-mm threshold was acquired in less than 5 min, achieving nearly 100% efficiency, showcasing its expeditious and robustness. In contrast, sFGE was acquired with a 5-mm threshold and had an average scan time of approximately 15 min. Overall image quality for MRCA was scored 3.3 for sFGE and 2.7 for cFGE without HR-DLR but increased to 3.6 for cFGE with HR-DLR and (p < 0.05). The clinical result of patients obtained within 5 min showed good quality images in both patients, even with a stent, without artifacts. Quantitative measures of SNR, CNR, and sharpness of vessel presented higher in cFGE with HR-DLR., Conclusion: Our findings demonstrate a robust, time-efficient solution for high-quality MRCA, enhancing patient comfort and increasing clinical throughput., Competing Interests: Declarations. Conflict of interest: Mr. Kutsuna and Mr. Yamashita are employees of Canon Medical Systems Corp., Japan. Dr. Ota and Dr. Miyazaki received Canon Medical research grants. All others have no conflict of interest. Ethical standards: Prior to examination, informed consent was obtained from all volunteers and patients. All experiments were performed in accordance with institutional guidance., (© 2024. The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB).)

The segmentation of airway from computed tomography (CT) images plays a vital role in pulmonary disease diagnosis, evaluation, surgical planning, and treatment. Nevertheless, it is still challenging for current methods to handle distal thin and low-contrast airways, leading to mis-segmentation issues. This paper proposes a detail-sensitive 3D-UNet (DS-3D-UNet) that incorporates two new modules into 3D-UNet to segment airways accurately from CT images. The feature recalibration module is designed to give more attention to the foreground airway features through a new attention mechanism. The detail extractor module aims to restore multi-scale detailed features by fusion of features at different levels. Extensive experiments were conducted on the ATM'22 challenge dataset composed of 300 CT scans with airway annotations to evaluate its performance. Quantitative comparisons prove that the proposed model achieves the best performance in terms of Dice similarity coefficient (92.6%) and Intersection over Union (86.3%), outperforming other state-of-the-art methods. Qualitative comparisons further exhibit the superior performance of our method in segmenting thin and confused distal bronchi. The proposed model could provide important references for the diagnosis and treatment of pulmonary diseases, holding promising prospects in the field of digital medicine. Codes are available at https://github.com/nighlevil/DS-3D-UNet/tree/master ., Competing Interests: Declarations Consent to participate All individual participants included in the study provided their informed consent. Conflict of interest The authors declare no competing interests., (© 2024. International Federation for Medical and Biological Engineering.)

Background: Ventriculoperitoneal (VP) shunts are commonly used for managing hydrocephalus, with mechanical dysfunction being the most common cause of complications that require revision. A VP shunt placed using a real-time three-dimensional (3D) robotic C-arm navigation system may have better outcomes and fewer complications., Methods: In this technical note, we introduced the workflow of the use of the real-time 3D robotic C-arm navigation system for ventriculoperitoneal shunting., Conclusion: The real-time 3D robotic C-arm can provide a more precise approach to the target. Furthermore, this technique may lower the risk of complications and increase the success rate of shunt placements., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.)

Background: Large variation in the prevalence of ankylosis and replacement resorption (ARR) is reported in the literature and most studies have relatively small patient numbers. The present retrospective study aimed to provide an overview on prevalence, location of, and associated risk factors with ARR based on a large sample of computed tomography (CT) / cone beam computed tomography (CBCT) scans of impacted teeth. The results should allow clinicians to better estimate the risk of ARR at impacted teeth., Methods: The CT/CBCT scans of 5764 patients of a single center in Central Europe were screened with predefined eligibility criteria. The following parameters were recorded for the finally included population: gender, age, tooth type/position, number of impacted teeth per patient, and presence/absence of ARR. For teeth with ARR the tooth location in reference to the dental arch, tooth angulation, and part of the tooth affected by ARR were additionally registered., Results: Altogether, 4142 patients with 7170 impacted teeth were included. ARR was diagnosed at 187 impacted teeth (2.6%) of 157 patients (3.7%); 58% of these patients were female and the number of teeth with ARR per patient ranged from 1 to 10. Depending on the tooth type the prevalence ranged from 0 (upper first premolars, lower central and lateral incisors) to 41.2% (upper first molars). ARR was detected at the crown (57.2%), root (32.1%), or at both (10.7%). After correcting for confounders, the odds for ARR significantly increased with higher age; further, incisors and first/second molars had the highest odds for ARR, while wisdom teeth had the lowest. More specifically, for 20-year-old patients the risk for ARR at impacted incisors and first/second molars ranged from 7.7 to 10.8%, but it approximately tripled to 27.3-35.5% for 40-year-old patients. In addition, female patients had significantly less often ARR at the root, while with increasing age the root was significantly more often affected by ARR than the crown., Conclusion: ARR at impacted teeth is indeed a rare event, i.e., only 2.6% of 7170 impacted teeth were ankylosed with signs of replacement resorption. On the patient level, higher age significantly increased the odds for ARR and on the tooth level, incisors and first/second molars had the highest odds for ARR, while wisdom teeth had the lowest., (© 2024. The Author(s).)

Purpose: In the absence of an intraoperative CT or MRI setup, post-implantation confirmation of electrode position in deep brain stimulation (DBS) requires patient transportation to the radiology unit, prolonging surgery time. This project aims to validate intraoperative 3D fluoroscopy (3DF), a widely available tool in Neurosurgical units, as a method to determine final electrode position., Methods: We performed a retrospective study including 64 patients (124 electrodes) who underwent DBS at our institution. Intraoperative 3DF after electrode implantation and postoperative volumetric CT were acquired. The Euclidean coordinates of the electrode tip displayed in both imaging modalities were determined and inter-method deviations were assessed. Pneumocephalus was quantified and its potential impact in determining the electrode position analyzed. Finally, 3DF and CT-imposed exposure to radiation was compared., Results: The difference between the electrode tip estimated by 3DF and CT was 0.85 ± 0.03 mm, and not significantly different (p = 0.11 for the distance to MCP assessed by both methods), but was, instead, highly correlated (p = 0.91; p < 0.0001). Even though pneumocephalus was larger in 3DF (6.89 ± 1.76 vs 5.18 ± 1.37 mm 3 in the CT group, p < 0.001), it was not correlated with the difference in electrode position measured by both techniques (p = 0.17; p = 0.06). Radiation exposure from 3DF is significantly lower than CT (0.36 ± 0.03 vs 2.08 ± 0.05 mSv; p < 0.0001)., Conclusions: Intraoperative 3DF is comparable to CT in determining the final DBS electrode position. Being a method with fewer radiation exposure, less expensive, faster and that avoids patient transportation outside the operation room, it is a valid tool to replace postoperative CT., (© 2024. The Author(s).)

Our objective was to investigate the long-term functional outcomes of robot-assisted partial nephrectomy (RAPN) combined with three-dimensional (3D) imaging. The 3D images, reconstructed using computed tomography, were introduced in RAPN procedures. The demographic, oncological, functional, and volumetric outcomes of 296 patients who underwent RAPN with and without 3D imaging between 2013 and 2021 were analyzed retrospectively. Propensity score matching (1:1) was performed to adjust for potential baseline confounders. After matching, 71 patients were allocated to each group. In the 3D RAPN (3DRPN) group, functional outcomes significantly improved: the number of patients with over 90% estimated glomerular filtration rate (eGFR) preservation rate (40 vs. 43, P = 0.044), eGFR preservation rate (88.0% vs. 91.6%, P = 0.006), the number of patients with chronic kidney disease (CKD) upstaging (26 vs. 13, P = 0.023), and split renal function preservation rate (operated kidney: 84.9% vs. 88.5%, P = 0.015). The 3DRPN group showed superiority in terms of >90% eGFR preservation (P = 0.010), CKD upstaging-free survival rates (P < 0.001), and volumetric outcomes (excess parenchymal volume: 27.9 vs. 17.7 mL, P = 0.030; parenchyma volume preservation rate: 81.6% vs. 88.8%, P = 0.006). Three-dimensional imaging was positively associated with eGFR preservation (P = 0.023, odds ratio: 2.34) and prevention of CKD upstaging (P = 0.013, odds ratio: 2.90). In this study, RAPN combined with 3D imaging underscored the preservation of eGFR > 90% and the prevention of CKD upstaging by improving the preservation rate of renal parenchyma and split renal function., (© 2024. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.)

The use of 3-dimensional (3D) technology has become increasingly popular across different surgical specialities to improve surgical outcomes. 3D technology has the potential to be applied to robotic assisted radical prostatectomy to visualise the patient's prostate anatomy to be used as a preoperative and peri operative surgical guide. This literature review aims to analyse all relevant pre-existing research on this topic. Following PRISMA guidelines, a search was carried out on PubMed, Medline, and Scopus. A total of seven studies were included in this literature review; two of which used printed-3D models and the remaining five using virtual augmented reality (AR) 3D models. Results displayed variation with select studies presenting that the use of 3D models enhances surgical outcomes and reduces complications whilst others displayed conflicting evidence. The use of 3D modelling within surgery has potential to improve various areas. This includes the potential surgical outcomes, including complication rates, due to improved planning and education., (© 2024. The Author(s).)

Introduction: Thermal imaging can be used for the non-invasive detection of blood vessels of the skin. However, mapping the results to the patient currently lacks user-friendliness. Augmented reality may provide a useful tool to superimpose thermal information on the patient., Methods: A system to support planning in reconstructive surgery using a thermal camera was designed. The obtained information was superimposed on the physical object using a Microsoft HoloLens. An RGB, depth, and thermal camera were combined to capture a scene of different modalities and reconstruct a virtual scene in real time. To register the different cameras and the AR device, an active calibration target was developed and evaluated. A Vuforia marker was used to register the hologram in the virtual space. The accuracy of the projected hologram was evaluated in a laboratory setting with participants by measuring the error between the physical object and the hologram., Results: The AR-based system was evaluated by 21 participants in a laboratory setting. The mean projection error is 10.3 ± 9.4 mm. The system is able to stream a three-dimensional scene with augmented thermal information in real time at 5 frames per second. The active calibration target can be used independently of the environment., Conclusion: The calibration target provides an easy-to-use method for the registration of cameras capturing the visible to long-infrared spectral range. The inside-out tracking of the HoloLens in combination with a Vuforia marker is not accurate enough for the intended clinical use., (© 2024. CARS.)

Objectives: To assess the feasibility of sodium-23 MRI for performing quantitative and non-invasive measurements of total sodium concentration (TSC) and relaxation in a variety of abdominal organs., Materials and Methods: Proton and sodium imaging of the abdomen was performed in 19 healthy volunteers using a 3D cones sequence and a sodium-tuned 4-rung transmit/receive body coil on a clinical 3 T system. The effects of B 1 non-uniformity on TSC measurements were corrected using the double-angle method. The long-component of 23 Na T 2 * relaxation time was measured using a series of variable echo-times., Results: The mean and standard deviation of TSC and long-component 23 Na T 2 * values were calculated across the healthy volunteer group in the kidneys, cerebrospinal fluid (CSF), liver, gallbladder, spleen, aorta, and inferior vena cava., Discussion: Mean TSC values in the kidneys, liver, and spleen were similar to those reported using 23 Na-MRI previously in the literature. Measurements in the CSF and gallbladder were lower, potentially due to the reduced spatial resolution achievable in a clinically acceptable scan time. Mean long-component 23 Na T 2 * values were consistent with previous reports from the kidneys and CSF. Intra-population standard error was larger in smaller, fluid-filled structures due to fluid motion and partial volume effects., (© 2024. The Author(s).)

Purpose: The segmentation of the heart and great vessels in CT images of congenital heart disease (CHD) is critical for the clinical assessment of cardiac anomalies and the diagnosis of CHD. However, the diverse types and abnormalities inherent in CHD present significant challenges to comprehensive heart segmentation., Methods: We proposed a novel two-stage segmentation approach, integrating a Convolutional Neural Network (CNN) with a postprocessing method with conditioned energy function for pulmonary and aorta. The initial stage employs a CNN enhanced by a gated self-attention mechanism for the segmentation of five primary heart structures and two major vessels. Subsequently, the second stage utilizes a conditioned energy function specifically tailored to refine the segmentation of the pulmonary artery and aorta, ensuring vascular continuity., Results: Our method was evaluated on a public dataset including 110 3D CT volumes, encompassing 16 CHD variants. Compared to prevailing segmentation techniques (U-Net, V-Net, Unetr, dynUnet), our approach demonstrated improvements of 1.02, 1.04, and 1.41% in Dice Coefficient (DSC), Intersection over Union (IOU), and the 95th percentile Hausdorff Distance (HD95), respectively, for heart structure segmentation. For the two great vessels, the enhancements were 1.05, 1.07, and 1.42% in these metrics., Conclusion: The outcomes on the public dataset affirm the efficacy of our proposed segmentation method. Precise segmentation of the entire heart and great vessels can significantly aid in the diagnosis and treatment of CHD, underscoring the clinical relevance of our findings., (© 2024. CARS.)

The aim of this study was to evaluate the bone tissue effects under dynamic loading using finite element analysis (FEA) for four angled abutments with different deviated palatal lateral tilt angles. A three-dimensional model of the posterior maxillary region and an implant crown model were reconstructed and assembled with a three-dimensional model of the implant, angled abutment, and central screw to create a total of 10 three-dimensional finite element models tilted at 15 ∘ , 20 ∘ , 25 ∘ , and 30 ∘ in three groups, and the dynamic loads simulating oral mastication were loaded on the implant crown to analyze the equivalent stresses and strains in the peri-implant bone tissues. Under the dynamic loading, the cortical bone on the buccal side of the implant neck showed different degrees of stress concentration, and the cortical bone stress was much higher than the cancellous bone, and the strain concentration area of each model was located in the bone tissue around the implant neck and base. For the use of angular abutment, under the premise that the cortical bone stresses and strains of the 10 models meet the requirements for use, the peak stresses of 2.907 MPa, 3.018 MPa, and 2.164 MPa were achieved by using the 20 ∘ angular abutment to achieve the tilt angles of 20 ∘ , 25 ∘ , and 30 ∘ implantation, which is more advantageous compared with other models., (© 2024. International Federation for Medical and Biological Engineering.)

Purpose: Calibration of an optical see-through head-mounted display is critical for augmented reality-based surgical navigation. While conventional methods have advanced, calibration errors remain significant. Moreover, prior research has focused primarily on calibration accuracy and procedure, neglecting the impact on the overall surgical navigation system. Consequently, these enhancements do not necessarily translate to accurate augmented reality in the optical see-through head mount due to systemic errors, including those in calibration., Method: This study introduces a simulated augmented reality-based calibration to address these issues. By replicating the augmented reality that appeared in the optical see-through head mount, the method achieves calibration that compensates for augmented reality errors, thereby reducing them. The process involves two distinct calibration approaches, followed by adjusting the transformation matrix to minimize displacement in the simulated augmented reality., Results: The efficacy of this method was assessed through two accuracy evaluations: registration accuracy and augmented reality accuracy. Experimental results showed an average translational error of 2.14 mm and rotational error of 1.06° across axes in both approaches. Additionally, augmented reality accuracy, measured by the overlay regions' ratio, increased to approximately 95%. These findings confirm the enhancement in both calibration and augmented reality accuracy with the proposed method., Conclusion: The study presents a calibration method using simulated augmented reality, which minimizes augmented reality errors. This approach, requiring minimal manual intervention, offers a more robust and precise calibration technique for augmented reality applications in surgical navigation., (© 2024. CARS.)

Objective: Performance assessments of quantitative determinations of proton density fat fraction (PDFF) have largely focused on the range between 0 and 50%. We evaluate PDFF in a two-site phantom study across the full 0-100% PDFF range., Materials and Methods: We used commercially available 3D chemical-shift-encoded water-fat MRI sequences from three MRI system vendors at 1.5T and 3T and conducted the study across two sites. A spherical phantom housing 18 vials spanning the full 0-100% PDFF range was used. Data at each site were acquired using default parameters to determine same-day and different-day intra-scanner repeatability, and inter-system and inter-site reproducibility, in addition to linear regression between reference and measured PDFF values., Results: Across all systems, results demonstrated strong linearity and minimal bias. For 1.5T systems, a pooled slope of 0.99 with a 95% confidence interval (CI) of 0.981-0.997 and a pooled intercept of 0.61% PDFF with a 95% CI of 0.17-1.04 were obtained. Results for pooled 3T data included a slope of 1.00 (95% CI 0.995-1.005) and an intercept of 0.69% PDFF (95% CI 0.39-0.97). Inter-site and inter-system reproducibility coefficients ranged from 2.9 to 6.2 (in units of PDFF), while intra-scanner same-day and different-day repeatability ranged from 0.6 to 7.8., Discussion: PDFF across the 0-100% range can be reliably estimated using current commercial offerings at 1.5T and 3T., (© 2024. The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB).)

Purpose: Motor neuron disease (MND) causes damage to the upper and lower motor neurons including the motor cranial nerves, the latter resulting in bulbar involvement with atrophy of the tongue muscle. To measure tongue atrophy, an operator independent automatic segmentation of the tongue is crucial. The aim of this study was to apply convolutional neural network (CNN) to MRI data in order to determine the volume of the tongue., Methods: A single triplanar CNN of U-Net architecture trained on axial, coronal, and sagittal planes was used for the segmentation of the tongue in MRI scans of the head. The 3D volumes were processed slice-wise across the three orientations and the predictions were merged using different voting strategies. This approach was developed using MRI datasets from 20 patients with 'classical' spinal amyotrophic lateral sclerosis (ALS) and 20 healthy controls and, in a pilot study, applied to the tongue volume quantification to 19 controls and 19 ALS patients with the variant progressive bulbar palsy (PBP)., Results: Consensus models with softmax averaging and majority voting achieved highest segmentation accuracy and outperformed predictions on single orientations and consensus models with union and unanimous voting. At the group level, reduction in tongue volume was not observed in classical spinal ALS, but was significant in the PBP group, as compared to controls., Conclusion: Utilizing single U-Net trained on three orthogonal orientations with consequent merging of respective orientations in an optimized consensus model reduces the number of erroneous detections and improves the segmentation of the tongue. The CNN-based automatic segmentation allows for accurate quantification of the tongue volumes in all subjects. The application to the ALS variant PBP showed significant reduction of the tongue volume in these patients and opens the way for unbiased future longitudinal studies in diseases affecting tongue volume., (© 2024. The Author(s).)